:github_url: https://github.com/pytorch/pytorch


.. _program_listing_file_torch_csrc_api_include_torch_optim_schedulers_lr_scheduler.h:

Program Listing for File lr_scheduler.h
=======================================

|exhale_lsh| :ref:`Return to documentation for file <file_torch_csrc_api_include_torch_optim_schedulers_lr_scheduler.h>` (``torch/csrc/api/include/torch/optim/schedulers/lr_scheduler.h``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   #pragma once
   
   #include <torch/optim/optimizer.h>
   
   #include <torch/csrc/Export.h>
   
   namespace torch::optim {
   
   class TORCH_API LRScheduler {
    public:
     // This class needs to take a reference of an optimizer from outside such that
     // it can modify its learning rates; due to this the lifetime of said
     // optimizer must be maintained
     LRScheduler(torch::optim::Optimizer& optimizer);
   
     virtual ~LRScheduler() = default;
   
     void step();
   
    protected:
     // A vector of learning rates is calculated and returned from the specific
     // subclass. A vector is returned with each element being a separate learning
     // rate for each param group - although the normal use case would be to return
     // a vector of identical elements.
     virtual std::vector<double> get_lrs() = 0;
   
     // Get current learning rates from the optimizer
     std::vector<double> get_current_lrs() const;
   
     unsigned step_count_{};
   
    private:
     void set_optimizer_lrs(const std::vector<double>& learning_rates);
   
     // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
     torch::optim::Optimizer& optimizer_;
   };
   } // namespace torch::optim